# TraVeLGAN: Image-to-image Translation by Transformation Vector Learning

## TL;DR

TraVeLGAN introduces a novel three-network GAN framework that preserves semantic transformations in a learned latent space, enabling effective image-to-image translation across complex, heterogeneous domains without cycle-consistency constraints.

## Contribution

The paper presents a new GAN architecture with a siamese network that allows translation between complex domains characterized by high-level shapes and contexts, surpassing style/texture limitations.

## Key findings

- Enables translation between heterogeneous domains with significant differences.
- Removes the need for cycle-consistency constraints.
- Achieves better semantic preservation in complex translations.

## Abstract

Interest in image-to-image translation has grown substantially in recent years with the success of unsupervised models based on the cycle-consistency assumption. The achievements of these models have been limited to a particular subset of domains where this assumption yields good results, namely homogeneous domains that are characterized by style or texture differences. We tackle the challenging problem of image-to-image translation where the domains are defined by high-level shapes and contexts, as well as including significant clutter and heterogeneity. For this purpose, we introduce a novel GAN based on preserving intra-domain vector transformations in a latent space learned by a siamese network. The traditional GAN system introduced a discriminator network to guide the generator into generating images in the target domain. To this two-network system we add a third: a siamese network that guides the generator so that each original image shares semantics with its generated version. With this new three-network system, we no longer need to constrain the generators with the ubiquitous cycle-consistency restraint. As a result, the generators can learn mappings between more complex domains that differ from each other by large differences - not just style or texture.